Systems and Methods of Detecting, Measuring, and Extracting Signatures of Signals Embedded in Social Media Data Streams

ABSTRACT

A system for scoring micro-blogging messages is provided, including an extractor, and evaluator, a calculator, and a publisher. The extractor may be configured to receive micro-blogging messages, to detect messages containing terms of interest, to extract raw data, and to store the data in a database. The evaluator may be configured to access and parse the stored data into tokenized data, and to store the tokenized data in a database. The evaluator may also be configured to identify relevant micro-blogging messages; to tag message as indicative; and to filter messages from low-volume or malicious sources before being tagged as indicative. The calculator may be configured to access a sentiment dictionary; to calculate a sentiment score of the tokenized data, and to calculate a sentiment signature for a term of interest. The publisher may be configured to provide access to clients of the system.

This application claims priority to and is a continuation-in-part ofU.S. Provisional Application Ser. No. 61/595,975, titled SYSTEMS ANDMETHODS OF DETECTING, MEASURING, AND EXTRACTING SIGNATURES OF SIGNALSEMBEDDED IN SOCIAL MEDIA DATA STREAMS, the disclosure of which isincorporated by reference.

BACKGROUND

The epic growth of the social web has created rich data sources forpredictive analytics. The enormous volume and diversity of informationpropagating amongst large user communities on micro-blogging platforms,Twitter in particular, and the emergence of social media dataaggregation service providers such as GNIP, Topsy, and StockTwits,enable new, intriguing opportunities to leverage the information contentembedded in social media. In addition to providing huge volumes ofminable data for diverse commercial applications, social media sourcesenable real-time detection, surveillance, and estimates of social mediasignatures for events and entities, thus extending the transformation ofthese data beyond estimation of the social media “sentiment” expressedfor an entity.

The current practice of social media analytics has strongly focused ontechniques to estimate the sentiment component of an entity's signature.These techniques employ methods from the discipline of Natural LanguageProcessing (NLP), in varying degrees and complexity, to produce coarsegrained sentiment estimates, oriented in terms of “Positive”,“Negative”, or “Neutral” for an entity. One disadvantage of coarsegained estimates is that they are highly sensitive to the thresholdsselected to determine the entity's possible three state outcome.Further, such discrete estimates are not suitable to time seriesnormalization techniques, which allow the detection of changes from anentity's normal sentiment levels or the comparison of one entity'ssentiment level to another entity's level on a common measurement basis.The level of social media activity is also an important component of anentity's social media signature. Current analytic techniques estimate anentity's level or intensity of social media activity by counting thetotal number of “mentions” of the entity present in micro-blogging datastreams observed during a time interval or converting the total to anaverage value observed over the interval. However, an activity metricdriven solely by the total number of counts does not readily showsignificant deviations from the entity's normal level of social mediaactivity, nor does such a representation allow for comparison of oneentity's activity level to another entity's level on a common basis. Anexample from the domain of stock trading is Apple Inc., stock symbolAAPL, which is consistently the most frequently mentioned stock onTwitter. A comparison of the total number of tweets observed during aday for AAPL to the total number observed for a stock with lessactivity, such as Caterpillar Inc. (CAT), is not useful because tweetvolumes for AAPL will always dominate, compared to CAT, over anyobservation interval.

SUMMARY

In one aspect of the present invention, certain disadvantages of knowntechniques may be remedied by measuring an entity's sentiment on acontinuous, normalized scale, enabling entity-to-entity comparisons anddetection of changes in an entity's sentiment level relative to asentiment universe, such as a broad market sentiment index or a marketsector sentiment index as an example drawn from the financial marketsand trading application domain. In another aspect of the presentinvention, certain disadvantages of known techniques may be remedied byexpressing a stock ticker's level of social media activity on acontinuous, normalized scale, enabling the direct comparison to otherstocks, using a common metric, and the detection of significant changesof the stock ticker's activity level relative to the constituents of amarket sector or broad market index.

In one example, a system for scoring micro-blogging messages includes aprivate infrastructure and a public infrastructure. The privateinfrastructure includes an extractor, and evaluator, a calculator, and apublisher. The extractor may be configured to receive micro-bloggingmessages via an application programming interface of at least onemicro-blogging source, to access a set of terms of interest, to detectreceived micro-blogging messages having least one of the terms ofinterest, to extract raw data from the micro-blogging messages, and tostore the extracted raw data in a private database, the extracted rawdata including at least a posting account, a posting time, and messagecontents.

The evaluator may be configured to access the stored raw data, to parsethe stored raw data into tokenized data, and to store the tokenized datain the private database. The evaluator may also be configured to detectterms of interest mentioned in the tokenized data to identify relevantmicro-blogging messages, to tag in the private database micro-bloggingmessages that contain a term of interest as being relevant to that termof interest, and using a posting account of a micro-blogging message totag the micro-blogging message as indicative. Micro-blogging messagesmay be filtered to exclude micro-blogging messages from low-volume ormalicious sources before the remaining messages are tagged asindicative. The evaluator may also be configured to eliminate duplicatemicro-blogging messages.

The calculator may be configured to access a sentiment dictionary and toaccess the stored tokenized data of micro-blogging messages tagged asindicative. The calculator may be further configured to calculate asentiment score based on sentiment values of words and phrases in thetokenized data for micro-blogging messages tagged as indicative, and tocalculate a vector of sentiment metrics for a term of interest based onthe sentiment scores associated with the term of interest and fallingwithin a predetermined lookback period, the calculator furtherconfigured to store the vector of sentiment metrics in the privatedatabase. The publisher may be configured to access the vectors ofsentiment metrics corresponding to a plurality of micro-bloggingmessages and to provide access to the vectors of sentiment metrics.

The public infrastructure may include a public database, the publicdatabase being populated with vectors of sentiment metrics from thepublisher and providing access to the sentiment metric to clients of thesystem for scoring micro-blogging messages.

In one example, the vector of sentiment metrics comprises at least anormalized representation of a sentiment time series score over thepredetermined lookback period, a smoothed weighted average of asentiment time series score over the predetermined lookback period, avolume of indicative micro-blogging messages, and a change in volume ofindicative micro-blogging messages relative to the average volume levelof relevant micro-blogging messages. In another example, the vector ofsentiment metrics comprises at least a normalized representation of asentiment time series score over the predetermined lookback period and ameasure of diversity of posting accounts contributing to the sentimenttime series score. In another example, the vector of sentiment metricscomprises at least a normalized representation of a sentiment timeseries score over the predetermined lookback period and a measure of thechange in sentiment volume relative to the average level observed for asentiment Universe.

The vector of sentiment metrics may further comprise a plurality ofvectors of sentiment metrics, wherein each vector represents sentimentmetrics calculated periodically according a predetermined calculationinterval. In one example, the calculation interval is 15 minutes and thelookback period is a number of days.

In another example, a system for scoring micro-blogging messages may beimplemented in a relational database having a term table, a messagetable, a digest table, a digest score table and a sentiment dictionary.The system may also include an extractor, an evaluator, and acalculator.

The term table includes a set of terms of interest, such as but notlimited to stock ticker symbols. The extractor, may be configured toreceive micro-blogging messages via an application programming interfaceof at least one micro-blogging source, to detect received micro-bloggingmessages having least one of the terms of interest, to extract raw datafrom the micro-blogging messages, and to store the extracted raw data ina message table in the database, the extracted raw data including atleast a posting account, a posting time, and message contents.

The evaluator may be configured to access the stored raw data in themessage table, to parse the stored raw data into tokenized data, tostore the tokenized data in a digest table, to detect terms of interestmentioned in the tokenized data, to analyze the raw data for indicationsof relevance, and to flag indicative micro-blogging messages in a digestscore table.

The sentiment dictionary may include sentiment values associated withwords and phrases commonly expressed in relevant micro-bloggingmessages. The sentiment dictionary may be included with the calculator.

The calculator may be configured to access a sentiment dictionary and toaccess the stored digest score table. The calculator may be furtherconfigured to calculate a sentiment score based on sentiment values ofwords and phrases in the tokenized data for micro-blogging messagestagged as indicative, and to calculate a vector of sentiment metrics fora term of interest based on the sentiment scores associated with theterm of interest and falling within a predetermined lookback period. Thecalculator may be further configured to store the vector of sentimentmetrics in a vector table. In one example, at least one of the sentimentmetrics comprises a normalized representation of a sentiment time seriesscore.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the system architecture of the social mediasignature analysis system according to one example of the presentinvention.

FIG. 2 is a block diagram of the system data flow according to oneexample of the present invention.

FIG. 3 is a block diagram of an Extractor stage according to one exampleof the present invention.

FIG. 4a-4b shows a condensed schema of a Private database and a Publicdatabase which may be used in one example of the present invention.

FIG. 5 is a block diagram of an Evaluator stage according to one exampleof the present invention.

FIG. 6 is a block diagram of a Calculator stage according to one exampleof the present invention.

FIG. 7 presents the results of exploratory data analysis to support theusefulness of an example of the disclosed invention.

FIG. 8 shows the results of applying the sentiment signatures of oneexample of the disclosed invention in a portfolio management scenario.

FIG. 9 shows risk adjusted performance measures, in terms of SharpeRatios, for the cumulative portfolio returns shown in FIG. 8.

FIGS. 10a-10b show the behavior of the sentiment signatures of oneexample of the disclosed invention of a stock experiencing an unusuallevel of social media activity.

FIGS. 11a-11b show the behavior of the sentiment signatures of oneexample of the disclosed invention of a stock experiencing asignificant, unexpected news release.

FIGS. 12a-12d show the behavior of the sentiment signatures of oneexample of the disclosed invention to detect dynamic sentimentsignatures, evolving on intraday timescales, for a stock before, across,and after an earnings event.

FIGS. 13a-13b show the application of the normalized representation ofmicro-blogging volume, presented in the disclosed invention, to improveand reveal features of sentiment signatures embedded in heat mapvisualizations of stock market trading sentiment

FIGS. 14a-14b demonstrate and interpret the sentiment signature alertcapability according to one aspect of the disclosed invention.

DETAILED DESCRIPTION

Various aspects of the invention disclosed in the examples providedherein relate generally to the field of social media analytics andspecifically to the detection and measurement of the signature of termsof interest as expressed in social media data sources such as themicro-blogging platform, Twitter. One particularly advantageous domainof application is the financial markets and the entities are the stocksof companies of interest to financial market traders and investors.Various aspects of the invention provide a framework and methods toextract and interpret sentiment signatures for the stocks of companiesas expressed in the Twitter messaging stream. This framework can beextended and adapted to other application domains, such as media,marketing, and healthcare, where the expression of sentiment throughsocial media is important Throughout the description, for the purposesof explanation, specific details are set forth in order to provide athorough understanding of the present disclosure. It will be apparent,however, to one skilled in the art that the present disclosure may bepracticed without some of these specific details.

FIG. 1 shows the block diagram 100 of one example of a systemarchitecture of a sentiment signature analysis system of the presentinvention. The architecture is comprised of a Private Infrastructure 102and a Public Infrastructure 103. The Private Infrastructure 102 maycomprise a general purpose computer configured with a relationaldatabase. The relational database may comprise a MySQL database. ThePrivate Infrastructure 102 collects micro-blogging messagescorresponding to terms of interest. In one advantageous example, theterms of interest correspond to publicly traded entities. Terms ofinterest may be defined by a Universe of stock ticker symbols. In oneexample, the Universe is an extended set of stock ticker symbols modeledafter the constituents of the Russell 3000 index. It is contemplatedthat ticker symbols from European and Asian markets may also beincluded. The Private Infrastructure 102 may continuously cycle throughthe Universe list, polling Twitter, directly, or micro-blogging dataaggregation providers (including ONIP, Topsy, StockTwits) 101 formessages containing commentary on the list members. Price quote data forthe members of the stock Universe may be obtained by polling Yahoo!Finance or other suitable information source. The Private Infrastructure102 extracts micro-blogging messages, herein referred to by their commonname as “tweets”; evaluates the relevance of the received tweets to thestocks symbols of interest; calculates the sentiment score of thereceived tweets and their contribution to vectors of sentiment metricsreferred to herein as sentiment signatures, for the members of theUniverse list; stores the results of scoring and signature analysis inthe relational database; and finally distributes the signature resultsto the Public Infrastructure 103 via FTP transfer for public clientaccess and also publishes the signature results via an API for privateclient access.

The Public Infrastructure 103 maintains a relational database 106 ofsentiment signature results and enables public client access 104 toreal-time and historical sentiment signatures for stocks that aremembers of the Universe list. The relational database 106 may comprise aMySQL database or any other suitable relational database. The PublicInfrastructure may further comprise a web server configured with HTMLcode and/or PHP scripts. Public clients are able to access sentimentestimates using a web browser interface; access sentiment estimatesusing mobile devices, tablets and smartphones; receive daily pre andpost market sentiment reports via email; establish stock watch lists toset sentiment signature alerts levels and receive email alerts ifsentiment thresholds or target values are met for stocks on a client'swatch list; receive real-time sentiment signatures by subscription to anRSS feed; or access historical sentiment signature data via an FTPinterface.

FIG. 2 is a data flow diagram 200 to illustrate the three stages neededto transform the tweet data stream into sentiment signatures for thestock Universe. The Extractor 201 polls the Twitter API or the API's ofthe micro-blogging data aggregators (as illustrated in FIG. 1) andcaptures groups of tweets that mention the stocks of interest. Theseraw, captured tweets populate tables in the Private database 105. TheEvaluator 202 analyzes each tweet and develops a list of tweets withfinancial market relevance to the terms of interest, in this example,stock ticker symbols. These are called “indicative” tweets, as theseindicate expressions of market trading sentiment for these stocks, andare passed to the Calculator 203, which determines their contribution tothe sentiment signatures. In one example, The Extractor 201, Evaluator202 and Calculator 203 may be implemented as JAVA applications. Otherprogramming environments or languages suitable for interfacing with arelational database may also be used.

One example of a vector of sentiment metrics is known as S-Factors.S-Factors are a family of metrics 204, which measure and represent thesocial media signature of an entity as a function of time. S-Factors aredefined as follows.

S-Score is the normalized representation of a sentiment time series,S(t), over a lookback period of W days. S(t) is computed by summing thesentiment level of all indicative tweets, N, contributing to an entity'ssentiment signature during an observation interval, [t−L, t], where L isthe length of the interval.

$\begin{matrix}{{{S(t)} = {\sum\limits_{i = 1}^{N}{{Sentiment}_{tweet}\left( t_{i} \right)}}}{{{{where}\text{:}\mspace{14mu} t} - L} < t_{i} < t}} & {{Equation}\mspace{14mu} (1)}\end{matrix}$

S-Score is the z-score representation of the time series, S(t), createdto establish a common scale according to a local mean and standarddeviation within a sliding window of W days before time, t.

$\begin{matrix}{{S_{Score}(t)} = \frac{{S(t)} - {\mu \left( {S(t)} \right)}}{\sigma \left( {S(t)} \right)}} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

where, μ and σ are the mean and standard deviation of S(t) within thelookback period, [t−W, t]. It is important that this normalization usedata only from the lookback period and does not include data from timesafter t, as this would inject a “look ahead” bias in the normalized timeseries.

The z-score representation transforms a time series to show fluctuationsaround a zero mean level and expressed on a scale of 1 standarddeviation. The z-score representation enables entity-to-entitycomparisons of sentiment on a common measurement scale. S-Score is ameasure of sentiment deviation from a normal state. Typical values rangefrom −2 to 2, with values greater than 4 or less than −4 indicatingextreme positive or negative states.

S-Scores are computed either weighted or un-weighted with respect to thearrival times of contributing tweets. Un-weighted means the contributingtweet scores are used directly as the summation is evaluated. Weightedmeans exponential scaling is applied to place stronger emphasis onrecently arrived tweets as the summation is evaluated. A tweet'ssentiment score is scaled by an exponential weight function that variesas a function of the arrival time.

The weight function has a maximum at the time of the stock's sentimentestimate and decreases smoothly to a minimum at the start of theobservation window, L periods prior.

S-Mean is a smoothed weighted average of a sentiment time series, S(t),over a lookback period of W days.

$\begin{matrix}{{{S_{Mean}(t)} = {\frac{1}{W}{\sum\limits_{i = 1}^{W}{\alpha_{i}{S\left( t_{i} \right)}}}}}{{{{{where}\text{:}\mspace{14mu} t} - W} < t_{i} < t},{\alpha_{i}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {weight}\mspace{14mu} {{function}.}}}} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

S-Mean is viewed as a measure of the current normal sentiment state. Achange in S-Mean is a sign of a change in trend. It is not a measure ofintensity but rather of a directional change. Typical values rangebetween 0 and 4 with the majority less than 1.

S-Delta is the percent change in S-Score over a lookback period of Wdays, and is a first order measurement of the sentiment trend. Amonotonic S-Delta over a short period of time can be a strongerindicator of changing sentiment levels.

$\begin{matrix}{{S_{Delta}(t)} = {\frac{{S_{Score}(t)} - {S_{Score}\left( {t - W} \right)}}{S_{Score}(t)}.}} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

S-Volatility is a percent measurement of the variability of thesentiment level over a lookback period of W days.

S _(Volatility)(t)=σ(S _(score)(t)).  Equation (5)

where, σ is the standard deviation of S-Score within the lookbackperiod, [t−W, t].

S-Volume is the volume of indicative tweets contributing to a sentimentsignature at an observation time, t. This metric has a range between 10and 1,000 for most stocks. Note that actual tweets volume can be 10× orgreater. The Evaluator 202 imposes a strong filter and only allowstweets that have financial market relevance to contribute to a stock'ssentiment signature. A rapid change in S-Volume is a good indication ofunusual social media activity, but not necessarily of a change insentiment level. Many times S-Volume is dominated by the most activesecurities in social media such as AAPL, GOOG, or AMZN. A betterindicator is S-Volume-z, the z-score normalized representation of theS-Volume time series over a lookback period of W days,

$\begin{matrix}{{S_{{Volume} - z}(t)} = {\frac{{S_{Volume}(t)} - {\mu \left( {S_{Volume}(t)} \right)}}{\sigma \left( {S_{Volume}(t)} \right)}.}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

This representation enables the direct comparison of the activity of anystock to another, using a common basis, and the detection of significantchanges of a stock's activity level relative to the constituents of amarket sector or broad market index.

S-Dispersion is a measure of the diversity of the Twitter users that arethe source of indicative tweets contributing to a stock's sentimentsignature. A dispersion level of 1 indicates that all indicative tweetscome from distinct sources. A value near 0 implies that all countsoriginate from one or a small number of distinct sources.

$\begin{matrix}{{S_{Dispersion}(t)} = \frac{{\# \mspace{14mu} {of}\mspace{14mu} {contributing}\mspace{14mu} {sources}\mspace{14mu} {at}\mspace{14mu} {time}},t}{{\# \mspace{14mu} {of}\mspace{14mu} {indicative}\mspace{14mu} {tweets}\mspace{14mu} {at}\mspace{14mu} {time}},t}} & {{Equation}\mspace{14mu} (7)}\end{matrix}$

All else being equal, a high S-Dispersion is in general desirablecompared to a low level, indicating that a diverse range of sourcescontribute to a stock's sentiment signature. The case of lowS-Dispersion warrants a closer look to assess validity or credibility ofthe contributing sources.

S-Buzz is a measurement of abnormal activity. It compares a stock'schange in sentiment volume to the average volume level for the sentimentUniverse. Typical values range from 1 to 3, with 0 indicating almost noactivity and 1 indicating normal activity. Values greater than 3indicate an extreme, unusual level of social media activity relative tothe Universe.

At any time, t,

$\begin{matrix}{{{S_{BUZZ}(t)} = \sqrt{{A(t)} - B}}{{Here},}} & {{Equation}\mspace{14mu} (8)} \\{{A(t)} = \frac{{S_{Volume}(t)} - {\mu \left( {S_{Volume}\left( {t = {9\text{:}10\mspace{14mu} {AM}\mspace{14mu} {ET}}} \right)} \right)}}{\sigma \left( {S_{Volume}\left( {t = {9\text{:}10\mspace{14mu} {AM}\mspace{14mu} {ET}}} \right)} \right)}} & {{Equation}\mspace{14mu} (9)}\end{matrix}$

-   -   B is the average S-Volume-z for the Universe @ 9:10 AM Eastern        Time.

Where, μ and σ are the mean and standard deviation within the lookbackinterval, [t−W, t].

FIG. 3 presents detail on the Extractor stage 300 of the processingpipeline. Data Acquisition 302 continuously polls the Twitter API 301 orthe API's of the micro-blogging data aggregation providers to capturetweets containing commentary on the members of the stock Universe. FIG.4a is a condensed database schema showing data models for tweet capture,sentiment scoring, and signature estimates. The stock Universe isdefined in the TERM table 403 (FIG. 4a ). Data Acquisition 302 receivestweets as JSON document objects and populates the TWEET table 401 (FIG.4a ). Metadata Extraction 303 and Reference Data Extraction 304 extractfeatures of the tweets (language, location, posting time) and featuresof the Twitter user account that originated the tweets (user profile,followers, account rating) to populate the DIGEST table 402 (FIG. 4a ).

FIG. 5 presents detail on the Evaluator stage 500 of the processingpipeline. The goal of the Evaluator stage is to identify indicativetweets with sentiment scores that will contribute to an entity'ssentiment signature. A Natural Language Processing step (NLP) 501 mayuse known language parser tools such asnlp.stanford.edu/downloads/lex-parser.shtml, which may be adapted foruse in the domain of financial markets. The NLP performs tokenization oftweet text to identify words, phrases, and stock symbols in the capturedtweets. The tokenized tweets are passed to the Duplicate Detectionprocess 502, which works to eliminate duplicate tweets submitted fromthe same source and to identify the occurrence and sources of“re-tweets”. A re-tweet (re-blogging a message) is an acknowledgement ofa tweet by Twitter users other than the tweet's originator and areconsidered to be endorsements by the wider community of a tweet'scontent Duplicate Detection 502 and re-tweet policies work to reduce theinfluence of tweets originating from “spamming” users, reducing thenoise level of the tweet stream and improving signature estimates.

Subject Mention Detection 503 identifies the specific entities mentionedin the text of the tweets. Tweets having content that mentions membersof the sentiment Universe are labeled as “relevant” and are tagged forthe corresponding entities. Relevance Assessment 504 is the final stepand proceeds to analyze the set of relevant tweets with respect toratings, developed by the disclosed invention, for the Twitter accountsthat are the originators of the captured tweets. Operational experiencehas shown that 90% of the tweets, determined to be relevant, originatefrom about 10% of the observed Twitter accounts. The 10% portion ofaccounts that present relevant content are also high volume accounts,meaning that these accounts have much higher total number and frequencyof tweets presented over time compared to other accounts. Further,accounts originating tweets that have content determined to be NOTrelevant are typically from low volume, sporadically active Twitterusers. A filter may be applied to eliminate tweets from low volumeoriginators, or originators determined to be malicious, transforming theset of relevant tweets to the set of indicative tweets for the entitiesthat will be used in the signature estimation stage. The result of theEvaluator stage 500 populates the DIGEST SCORE table 404 (FIG. 4a ).

FIG. 6 presents detail on a Calculator stage 600 of the processingpipeline. The set of indicative tweets is received by the SentimentCalculation 601. Sentiment Calculation accesses the tokenized resultsfor each tweet, which identifies word boundaries and specific wordcontent. The sentiment level for each word parsed from a tweet isobtained from a Domain Specific Sentiment Dictionary. SentimentDictionary may be tuned for performance in the financial market domain.In one example, the Sentiment Dictionary has 18,000 words (uni-grams)and 225 two word phrases (bi-grams) that have content and sentimentlevels adapted to financial market terminology as expressed inmicro-blogging messages.

Thus, for a tweet containing n identified words and m identified twoword phrases, the tweet's sentiment score is the average value of thetotal identified sentiment content,

$\begin{matrix}{{Sentiment}_{tweet} = {\frac{1}{m + n}\left( {{\sum\limits_{i = 1}^{n}{{Sentiment}_{word}(i)}} + {\sum\limits_{j = 1}^{m}{{Sentiment}_{phrase}(j)}}} \right)}} & {{Equation}\mspace{14mu} (10)}\end{matrix}$

For an entity at time, t, the sentiment inferred from its associatedindicative tweets has been stated previously. Results are stored in theHIT_SCORE table 405 (FIG. 4a ).

Bucketing and Weighting 602 takes all scores for an entity's indicativetweets and groups these into time period buckets based on the createdtime stamps of the tweets as defined in the HIT_SCORE table 405 (FIG. 4a). For the i-th indicative tweet, a weight function is applied to thetweet's sentiment score, as

w _(i)(t)=1(no weighting) OR e ^((t) ^(arrival) ^(−t))(exponentialweighting)  Equation (11)

with exponential weighting placing more emphasis on recently arrivedtweets.

Normalization and Scoring 603 calculates the S-Score and other metricsdefined previously.

Aggregation Scoring 604 calculates S-Score and other metrics for marketindices, market sectors and industry groups by aggregating the S-Scoresof the constituent entities. The results populate the BUCKETED_SCOREtable 406 (FIG. 4a ). These processes are performed for all members ofthe Universe domain and yield S-Factors sentiment signatures 605.

The Distributor of 102 pushes sentiment signature vectors to the Publicdatabase 106, to populate the SUMMARY_BY_TICKER table (FIG. 4b ).

FIG. 7 presents initial exploratory data analysis 700 to support theusefulness of the disclosed invention. The analysis considers whetherpre-market-open sentiment signatures are predictors of post-market-openprice changes as shown by scatter plots of overnight (C-O) andopen-to-close (O-C) price changes, for securities with large S-Scores.This is shown in 701 for stocks with S-Scores >2.5 and in 702 for stockswith S-Scores <−2.5. In each regime, the stocks had daily close pricesgreater than $5/share.

The horizontal axes are the percentage overnight price change (C-O). Thevertical axes are the percentage open-to-close price change (O-C). Thesedata are observed from a sample space of 197,000 events for the stockUniverse captured during the first Quarter of 2012. The plots show theevents that satisfy the stated conditions on S-Score and price.

The scatter plots show that pre-market-open S-Score is a very goodestimator of the direction of market-at-open price changes. The scatterplot 701 for S-Score >2.5 has a large number of points in the right twoquadrants while the scatter plot 702 for S-Score <−2.5 has a largenumber of points in the left two quadrants. This simple observationimplies that Sentiment Calculation 601 employs accurate sentimentestimation algorithms.

The table 703 shows the relative frequency of open-to-close returns. Therelatively large frequency of moves of greater than +2% and less than−2%, demonstrates that the S-Score filtered returns distribution has fattails. For S-Score >2.5, 52% of open-to-close returns are up, 36.5% areup greater than 1% and 24% are up greater than 2%. For S-Score <−2.5,46.3% of open-to-close returns are down, 30% are down more than −1%, and18.5% are down greater than −2%. This observation implies that stockswith positive S-Scores tend to yield positive daily returns, while theconverse is the case for stocks with negative scores.

FIG. 8 presents the results of investing in portfolios of stocks withlarge positive or large negative S-Scores and illustrates the utility ofS-Factors to the financial market domain. The chart 800 shows thecumulative returns of portfolios established each day by purchasingstocks with large pre-market-open S-Scores estimated at 9:10 AM EasternTime. Each day, the portfolios are established at the market open andare liquidated at the market close. The observation period extends fromDec. 1, 2011 to Dec. 21, 2012. The top line 801 shows the cumulativereturns from stocks with positive S-Scores >2. The bottom line 803 showsthe cumulative returns from stocks with negative S-Scores <−2. Thedashed line 802 shows the cumulative returns over this period from amarket benchmark, SPY, which is the SPDR S&P 500 Exchange Traded Fundthat is designed to track the S&P 500 index.

The stocks with large positive S-Scores significantly outperformed SPYover this period, while the stocks with large negative S-Scoressignificantly underperformed relative to the market benchmark. A measureof the risk-adjusted performance for this sequence of portfolios isexpressed in terms of their annualized Sharpe Ratios 900 as shown inFIG. 9.

The Sharpe Ratios were computed at the 95% confidence level with the Rstatistical programming language using the PerformanceAnalytics package.

FIG. 10 shows the evolution of the sentiment signature 1000 for thestock of Netflix, Inc. (NFLX). During the week of Jan. 21, 2013, NFLXgained 71%, as the company reported quarterly earnings far higher thanWall Street analysts' estimates. Chart 1001 shows the graphical userinterface from the web site of the disclosed invention and displays thebehavior of the S-Score, S-Volume, and S-Mean metrics before and acrossthe earnings event. Chart 1002 correlates features of the sentimentsignature derived from micro-blogging message content and activity withthe behavior of Netflix's stock price.

FIG. 11 demonstrates the capability of the disclosed invention to detectsignificant corporate news events from micro-blogging message contentand activity 1100. Chart 1101 and chart 1102 show daily and intradayS-Score and S-Volume signatures for Best Buy Co., Inc. (BBY) on Aug. 6,2012.

In the pre-market of Aug. 6, 2012, Richard Schulze, Founder and formerChairman of Best Buy Co., Inc., submitted a written proposal to thecompany's Board of Directors to acquire all outstanding shares of thecompany that he did not already own for a price of $24.00 to $26.00 pershare in cash. The proposed purchase price was at a premium of 36% to47% to Best Buy's closing stock price of $17.64 on Aug. 3, 2012. At thetime, Schulze was Best Buy's largest shareholder, controlling 20.1% ofBBY shares.

The announcement occurred after 8:15 AM Eastern time. In this example,the S-Factor metrics are computed continuously at 15 minute bucketedintervals intraday. The S-Factor metrics generated for BBY that daydetected the leading edge of the social media signature of Schulze'sannouncement during the computation of the 8:30 AM bucket for Best Buy.Chart 1102 shows the dramatic change in Best Buy's S-Score and S-Volumelevels as the news event unfolded. At that time, the takeover bid wasviewed very favorably, resulting in a sharp transition to high positivesentiment levels and an unusual, rapid increase in social media activityfor the stock as indicated by S-Volume.

The major financial news services (Bloomberg, Reuters, and Wall StreetJournal) began publishing headline stories covering this developmentstarting at 8:44 AM. This event is an excellent example showing thatsocial media content and level of activity can be a leading indicatorrelative to traditional financial news services.

FIG. 12 shows the capability of the disclosed invention to detectdynamic sentiment signatures 1200 evolving on intraday timescales. Theprogression of intraday S-Score and S-Volume metrics for Facebook, Inc.(FB) is shown in 1200 (FIGS. 12a, 12b, 12c, 12d ) from Jul. 24 throughJul. 27, 2012.

The company reported EPS of $0.12, in-line with analysts' estimates, andrevenues of $1.18 billion, beating estimates by 2.6%. Yet, the stocksold off sharply, ending the week down 38% from its IPO price in May.The decline occurred amid concerns that revenues and user growth areslowing, significantly, and that business models remain immature, evenat Facebook, the model of excellence for the new era of socialnetworking, media, and advertising.

Facebook's sentiment signature entered a sustained down trend, startingaround Noon on July 24 and reached extreme negative levels after theclose on July 25. On July 26, the day of the earnings announcement,Facebook's S-Score maintained negative levels up to the time of theearnings data release at 3:05 PM Central Time.

After the announcement, sentiment levels relaxed to neutral after theclose on July 26 and into the open of July 27. At the open of July 27,Facebook's S-Score made a rapid decline to negative levels, reachinghigh negative levels around 10 AM Central Time. Facebook's stock closedthe day at $23.71, a decline of 11.8%.

FIG. 13 demonstrates the use of S-Volume-z, the z-score normalizedrepresentation of micro-blogging volume presented in the disclosedinvention, to reveal features of signatures embedded in heat mapvisualizations of stock market trading sentiment 1300. Chart 1301 showsthe current practice to construct a market sentiment map, observable atsites such as StockTwits.com. In this visualization, a stock's colorintensity level is mapped to some measure of trading sentiment for thestock, or to the stock's change in market price, or to the stock's sharevolume. The area on the map allocated to a particular stock isproportional to the raw number of micro-blogging messages observed forthe stock over an observation period. Thus, the stocks with the largesttotal number of messages will occupy the largest areas on the map. Chart1301 shows a typical scenario, observed on May 25, 2012, in which thestocks such as AAPL, GOOG, JPM, AMZN, and MSFT occupy large map areas,but have moderately positive, moderately negative, or neutral sentimentlevels. Stocks such as AAPL, GOOG, and AMZN will always dominate thecurrent visualization practice because these stocks consistently havehigh raw message volumes, each day, irrespective of sentiment content.Thus, the current practice has limited usefulness to detect significantchanges in sentiment signatures. On that day, stocks such as KLAC andULTR had extreme sentiment levels and significant changes from normallevels of message activity as measured by S-Volume-z, but are obscuredsimply because these had far fewer total number of micro-bloggingmessages compared to the dominate stocks.

The market map 1302 shows the result of rescaling where the areas are afunction of a stock's S-Volume-z metric. In this representation, anumber of stocks with extreme sentiment levels and unusual social mediaactivity, such as KLAC and ULTR, are detected and emerge from theclutter of 1301. JCP was retained in the rescaling visualization 1302because the stock had extreme negative sentiment and unusually highmessage volume on May 25. The high message volume stocks of that day,such as AAPL and JPM, occupy regions appropriate for their sentimentlevels and historical normal message volume levels.

FIG. 14 demonstrates the sentiment signature alert capability 1400 ofthe disclosed invention. The graphical user interface 1401 enables usersto set thresholds on any of four components of a stock's sentimentsignature. 1401 shows thresholds set for S-Score <−2, S-Mean >1.5,S-Buzz >2, and S-Volume >50 for the stock of Green Mountain CoffeeRoasters Inc. (GMCR). The alert structure sends an email to a user whenany of the thresholds set by the user are exceeded. The S-Volumethreshold was exceeded for GMCR at 11:49 AM Eastern Time on Jan. 24,2013, and generated the alert email message 1402. The chart 1403correlates the timing of the alert and other metrics with the behaviorof GMCR's stock price. The alert served as an early indicator ofincreased social media activity for the stock. Confirmation came in thepre-market of January 25 as GMCR's S-Score increased to high positivelevels and S-Volume continued to rise, increasing 78% above the levelsof the previous day. On January 25, GMCR stock closed trading at $46.31,up 5.78% for the day.

The foregoing examples are made for illustrative purposes, and not tolimit the present invention. The different embodiments and processes maybe implemented in software, including individual applications, combinedapplications, and scripting languages, firmware, hardware, orcombinations of the above.

1-20. (canceled)
 21. A method of calculating a vector of sentiment metrics in a computer system, the method comprising: receiving messages via an application programming interface of at least one social media data source, identifying messages that contain a term of interest from a set of terms of interest as being relevant to that term of interest, identifying messages from non-filtered posting accounts as indicative messages; calculating a sentiment score for messages identified as both relevant to a term of interest and indicative based on sentiment values of words and phrases in the messages; bucketing sentiment scores of the messages identified as both relevant to a term of interest and indicative based the term of interest and a time stamp of the messages; calculating a normalized sentiment time series score for the bucketed sentiment scores; calculating a vector of sentiment metrics for a term of interest based on the normalized sentiment time series score; and distributing the vectors of sentiment metrics.
 22. The method of claim 21, wherein low volume and malicious posting accounts are excluded from the non-filtered posting accounts.
 23. The method of claim 21, wherein the step of distributing the vectors of sentiment metrics further comprises pushing the vectors of sentiment metrics to a table in a public database.
 24. The method of claim 21, further comprising de-duplicating messages prior to calculating a sentiment score for messages.
 25. The method of claim 21, wherein the normalized sentiment time series scores are weighted based on time stamps of each message.
 26. The method of claim 25, wherein the weighting places more emphasis on newly arrived messages.
 27. The method of claim 25, wherein the weighting is exponential.
 28. The method of claim 21, wherein the set of terms of interest comprises a plurality of stock ticker symbols.
 29. The method of claim 21, wherein the set of terms of interest correspond to publicly traded entities.
 30. The method of claim 21, wherein the step of receiving messages via an application programming interface of at least one social media data source further comprises: extracting raw data from the messages into a private database, the extracted raw data including at least a posting account, a posting time, and message contents; and parsing the raw data into tokenized data.
 31. The method of claim 21, wherein the step of calculating a sentiment score for messages identified as both relevant to a term of interest and indicative based on sentiment values of words and phrases in the messages further comprises: accessing a domain-specific sentiment dictionary; and obtaining a sentiment level for the words and phrases in the messages.
 32. The method of claim 31, wherein the domain-specific sentiment dictionary has content and sentiment levels adapted to financial market terminology.
 33. The method of claim 21, wherein the at least one social media data source comprises at least one micro-blogging data source, and the messages comprise microblogging messages.
 34. A system for scoring messages from at least one social media data source, comprising: a term table in a relational database, the term table including a set of terms of interest; an extractor, configured to receive messages via an application programming interface of at least one social media data source, to detect received messages having least one of the terms of interest, to extract raw data from the messages, and to store the extracted raw data in a message table in the database, the extracted raw data including at least a posting account, a posting time, and message contents; an evaluator, configured to access the stored raw data in the message table, to parse the stored raw data into tokenized data, to store the tokenized data in a digest table, to detect terms of interest mentioned in the tokenized data, to analyze the raw data for indications of relevance, and to flag indicative messages in a digest score table; a sentiment dictionary, the sentiment dictionary including sentiment values associated with words and phrases commonly expressed in relevant messages; and a calculator, configured to access a sentiment dictionary and to access the stored digest score table, the calculator further configured to calculate a sentiment score based on sentiment values of words and phrases in the tokenized data for messages tagged as indicative, and to calculate a vector of sentiment metrics for an term of interest based on the sentiment scores associated with the term of interest and falling within a predetermined lookback period, the calculator further configured to store the vector of sentiment metrics in a vector table; wherein at least one of the sentiment metrics comprises a normalized representation of a sentiment time series score.
 35. The system of claim 34, wherein the vector of sentiment metrics further comprises the z-score representation of the time series as follows: S_Score(t)=(S(t)−(S(t)))/a(S(t)) where μS((t)) is the mean and σ(S(t)) is the standard deviation of a sentiment score during the predetermined time interval.
 36. The system of claim 34, wherein the vector of sentiment metrics comprises at least a normalized representation of a sentiment time series score over the predetermined lookback period, a smoothed weighted average of a sentiment time series score over the predetermined lookback period, a volume of indicative messages, and a change in volume of indicative messages relative to the average volume level of relevant messages.
 37. The system of claim 34, wherein the vector of sentiment metrics comprises at least a normalized representation of a sentiment time series score over the predetermined lookback period and a measure of diversity of posting accounts contributing to the sentiment time series score.
 38. The system of claim 34, wherein the vector of sentiment metrics comprises at least a normalized representation of a sentiment time series score over the predetermined lookback period and a measure of the change in sentiment volume relative compared to the average level observed for a sentiment Universe. 